How can I predict the true label for data with incomplete features based on the trained model with data with more features?
Assuming that you have access to the training data set, you could use an autoencoder network to predict what features f4, f5, f6 'could be' for the test data set. The way to do this is to train the autoencoder on the training data set with features f1, f2, f3 as inputs, and then use f1,f2,f3,f4,f5,f6 as the output of the network. The autoencoder then effectively learns to map any input samples with (f1,f2,f3) to (f1,f2,f3,f4,f5,f6). By passing your test data through the autoencoder, you can then use the output and pass it to your model.